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Computer aided process planning (CAPP) is generally acknowledged as a significant activity to achieve computer‐integrated manufacturing (CIM). In coping with the dynamic changes in the modern manufacturing environment, the awareness of developing intelligent CAPP systems has to be raised, in an attempt to generate more successful implementations of intelligent manufacturing systems. In this paper, the architecture of a hybrid intelligent inference model for implementing the intelligent CAPP system is developed. The detailed structure for such a model is also constructed. The establishment of the hybrid intelligent inference model will enable the CAPP system to adapt automatically to the dynamic manufacturing environment, with a view to the ultimate realization of full implementation of intelligent manufacturing systems in enterprises.

Mediating effects are often tested using hierarchical multiple regression (HMR) procedures. Typical of the HMR-based strategies is the very frequently cited and widely used procedure described by Baron and Kenny (1986). Unfortunately, there are several important problems with it. More specifically, as we demonstrate below, it: (a) is of virtually no value for buttressing claims of mediating effects for data from non-experimental research; (b) produces erroneous inferences about the existence of mediating effects for misspecified mediation models; and (c) is incapable of providing credible evidence of such effects in a large proportion of cases, even for properly specified mediation models. We detail a number of important implications of our analyses.

In this chapter, we explicate two related techniques that help quantify the sensitivity of a given causal inference to potential omitted variables and/or other sources of unexplained heterogeneity. In particular, we describe the Impact Threshold of a Confounding Variable (ITCV) and the Robustness of Inference to Replacement (RIR). The ITCV describes the minimum correlation necessary between an omitted variable and the focal parameters of a study to have created a spurious or invalid statistical inference. The RIR is a technique that quantifies the percentage of observations with nonzero effects in a sample that would need to be replaced with zero effects in order to overturn a given causal inference at any desired threshold. The RIR also measures the percentage of a given parameter estimate that would need to be biased in order to overturn an inference. Each of these procedures is critical to help establish causal inference, perhaps especially for research urgently studying the COVID-19 pandemic when scholars are not afforded the luxury of extended time periods to determine precise magnitudes of relationships between variables. Over the course of this chapter, we define each technique, illustrate how they are applied in the context of seminal strategic management research, offer guidelines for interpreting corresponding results, and delineate further considerations.

As the number of users on social network services (SNSs) continues to increase at a remarkable rate, privacy and security issues are consistently arising. Although users may not want to disclose their private attributes, these can be inferred from their public behavior on social media. In order to investigate the severity of the leakage of private information in this manner, the purpose of this paper is to present a method to infer undisclosed personal attributes of users based only on the data available on their public profiles on Facebook.

Facebook profile data consisting of 32 attributes were collected for 111,123 Korean users. Inferences were made for four private attributes (gender, age, marital status, and relationship status) based on five machine learning-based classification algorithms and three regression algorithms.

Experimental results showed that users’ gender can be inferred very accurately, whereas marital status and relationship status can be predicted more accurately with the authors’ algorithms than with a random model. Moreover, the average difference between the actual and predicted ages of users was only 0.5 years. The results show that some private attributes can be easily inferred from only a few pieces of user profile information, which can jeopardize personal information and may increase the risk to dignity.

In this paper, the authors’ only utilized each user’s own profile data, especially text information. Since users in SNSs are directly or indirectly connected, inference performance can be improved if the profile data of the friends of a given user are additionally considered. Moreover, utilizing non-text profile information, such as profile images, can help increase inference accuracy. The authors’ can also provide a more generalized inference performance if a larger data set of Facebook users is available.

A private attribute leakage alarm system based on the inference model would be helpful for users not desirous of the disclosure of their private attributes on SNSs. SNS service providers can measure and monitor the risk of privacy leakage in their system to protect their users and optimize the target marketing based on the inferred information if users agree to use it.

This paper investigates whether private attributes of SNS users can be inferred with a few pieces of publicly available information although users are not willing to disclose them. The experimental results showed that gender, age, marital status, and relationship status, can be inferred by machine-learning algorithms. Based on these results, an early warning system was designed to help both service providers and users to protect the users’ privacy.

The purpose of this paper is to examine the impact the US Supreme Court's Tellabs decision has had on Section 10 (b) cases generally and on cases related to subprime mortgage‐backed securities.

The paper provides background including provisions of Section 10(b) of the Securities Exchange Act of 1934 and the Private Securities Litigation Reform Act (PSLRA) of 1995. Analyzes the decision in Tellabs, Inc. v. Makor Issue & Rights, Ltd, recent decisions since Tellabs by the the Second, Seventh, and Ninth Circuits, and decisions in two recent subprime securities cases in which defendants moved to dismiss pursuant to Tellabs; and draws preliminary conclusions from cases that have applied Tellabs so far.

The PSLRA raised the bar for pleading scienter by requiring a “strong inference”, but the courts of appeals have differed in applying the standard. The Tellabs decision asserted that the PSLRA requires consideration of competing inferences in determination of whether scienter is adequately pleaded. The Supreme Court's “prescription” said the inference of scienter must be more than merely “reasonable” or “permissible”; it must seem to a reasonable person to be at least as cogent and compelling as any opposing inference. One lesson of Tellabs and the Seventh Circuit's decision on remand is that the “plausibility” of scienter allegations requires a fact‐specific inquiry. The Ninth Circuit court found that “deliberate recklessness” is sufficient to allege scienter. In one of the subprime cases, In re 2007 Novastar Financial, Inc., a district court found that a deterioration of a company's business was not evidence of wrongdoing. It is difficult – and premature – to draw firm conclusions from cases that have applied Tellabs so far, but requiring courts to consider competing inferences, requiring the pleadings in question to satisfy more “adjectives”, and requiring the “weighing” of inferences at the pleadings stage would all appear to help defendants. However, Tellabs could help plaintiffs by reversing previous practice in which a “tie” between competing inferences automatically resulted in a victory for the defendant.

The paper offers practical guidance by experienced securities lawyers.